Inverse Optimal Control with Speed Gradient for a Power Electric System Using a Neural Reduced Model

This paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in three different cases in the lines of transmission

Path Planning in Rough Terrain Using Neural Network Memory

Learning navigation policies in an unstructured terrain is a complex task. The Learning to Search (LEARCH) algorithm constructs cost functions that map environmental features to a certain cost for traversing a patch of terrain. These features are abstractions of the environment, in which trees, vegetation, slopes, water and rocks can be found, and the traversal costs are scalar values that represent the difficulty for a robot to cross given the patches of terrain. However, LEARCH tends to forget knowledge after new policies are learned. The study demonstrates that reinforcement learning and long-short-term memory (LSTM) neural networks can be used to provide a memory for LEARCH. Further, they allow the navigation agent to recognize hidden states of the state space it navigates. This new approach allows the knowledge learned in the previous training to be used to navigate new environments and, also, for retraining. Herein, navigation episodes are designed to confirm the memory, learning policy and hidden-state recognition capabilities, acquired by the navigation agent through the use of LSTM

Fast Chaotic Encryption for Hyperspectral Images

The information collected by hyperspectral images (HI) is essential in applications of remote sensing like object detection, geological process recognition, and identifying materials. However, HI information could be sensitive, and therefore, it should be protected. In this chapter, we show a parallel encryption algorithm specifically designed for HI. The algorithm uses multiple chaotic systems to produce a crossed multidimensional chaotic map for encrypting the image; the scheme takes advantage of the multidimensional nature of HI and is highly parallelizable, which leads to a time-efficient algorithm. We also show that the algorithm gets high-entropy ciphertext and is robust to ciphertext-only attacks

Particle Swarm Optimization Algorithm with a Bio-Inspired Aging Model

A Particle Swarm Optimization with a Bio-inspired Aging Model (BAM-PSO) algorithm is proposed to alleviate the premature convergence problem of other PSO algorithms. Each particle within the swarm is subjected to aging based on the age-related changes observed in immune system cells. The proposed algorithm is tested with several popular and well-established benchmark functions and its performance is compared to other evolutionary algorithms in both low and high dimensional scenarios. Simulation results reveal that at the cost of computational time, the proposed algorithm has the potential to solve the premature convergence problem that affects PSO-based algorithms; showing good results for both low and high dimensional problems. This work suggests that aging mechanisms do have further implications in computational intelligence

VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad

Acta de congresoLa conmemoración de los cien años de la Reforma Universitaria de 1918 se presentó como una ocasión propicia para debatir el rol de la historia, la teoría y la crítica en la formación y en la práctica profesional de diseñadores, arquitectos y urbanistas. En ese marco el VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad constituyó un espacio de intercambio y reflexión cuya realización ha sido posible gracias a la colaboración entre Facultades de Arquitectura, Urbanismo y Diseño de la Universidad Nacional y la Facultad de Arquitectura de la Universidad Católica de Córdoba, contando además con la activa participación de mayoría de las Facultades, Centros e Institutos de Historia de la Arquitectura del país y la región. Orientado en su convocatoria tanto a docentes como a estudiantes de Arquitectura y Diseño Industrial de todos los niveles de la FAUD-UNC promovió el debate de ideas a partir de experiencias concretas en instancias tales como mesas temáticas de carácter interdisciplinario, que adoptaron la modalidad de presentación de ponencias, entre otras actividades. En el ámbito de VIII Encuentro, desarrollado en la sede Ciudad Universitaria de Córdoba, se desplegaron numerosas posiciones sobre la enseñanza, la investigación y la formación en historia, teoría y crítica del diseño, la arquitectura y la ciudad; sumándose el aporte realizado a través de sus respectivas conferencias de Ana Clarisa Agüero, Bibiana Cicutti, Fernando Aliata y Alberto Petrina. El conjunto de ponencias que se publican en este Repositorio de la UNC son el resultado de dos intensas jornadas de exposiciones, cuyos contenidos han posibilitado actualizar viejos dilemas y promover nuevos debates. El evento recibió el apoyo de las autoridades de la FAUD-UNC, en especial de la Secretaría de Investigación y de la Biblioteca de nuestra casa, como así también de la Facultad de Arquitectura de la UCC; va para todos ellos un especial agradecimiento

Complex and Hypercomplex-Valued Support Vector Machines: A Survey

In recent years, the field of complex, hypercomplex-valued and geometric Support Vector Machines (SVM) has undergone immense progress due to the compatibility of complex and hypercomplex number representations with analytic signals, as well as the power of description that geometric entities provide to object descriptors. Thus, several interesting applications can be developed using these types of data and algorithms, such as signal processing, pattern recognition, classification of electromagnetic signals, light, sonic/ultrasonic and quantum waves, chaos in the complex domain, phase and phase-sensitive signal processing and nonlinear filtering, frequency, time-frequency and spatiotemporal domain processing, quantum computation, robotics, control, time series prediction, and visual servoing, among others. This paper presents and discusses the importance, recent progress, prospective applications, and future directions of complex, hypercomplex-valued and geometric Support Vector Machines

Neural Identifier using Super-Twisting Differentiator Training Algorithm

Artificial neurons are a mathematical abstraction of biological neurons. The model of an artificial neuron considers the main characteristics of a biological neuron and represents them as the weights, an adder, and an activation function, where the knowledge learned is stored in the adjusted weights. Artificial neural networks (ANNs) are structures of interconnected artificial neurons. There exist learning algorithms, which provide rules for the adjusting weights, called training algorithms. Commonly, ANNs are used to solve problems of classification, pattern recognition, function approximation, control, among others. Often, ANNs are trained with learning rules based on information from error derivatives with respect to the weights. Backpropagation (BP) is based on information from the first-order derivative, and probably is the well-known training algorithm, but has a slow error convergence. Last two decades, training algorithms based on extended Kalman filter (EKF), which is faster than BP, have been exploited. However, the calculation of the derivatives using the EKF algorithm requires high computational resources. To avoid the derivatives calculation, in this work an online training algorithm based on the discrete-time super-twisting (ST) differentiator is proposed. Super-twisting is a control technique from sliding modes, which attenuates chattering, i.e., the high-frequency oscillation effects. This technique consists in designing a sliding variable of relative degree r=1, that, with a discontinuous control action, the sliding variable is forced to zero in finite time and remains at zero even in the presence of perturbation and uncertainties. Recently, in it is proposed a discrete-time super-twisting differentiator training algorithm. However, they used a classic ST structure, besides, with a high complexity to be implemented. In contrast, in the work we present, it is used a different ST structure proposed in which improves the convergence time, and in an easier way to implement. Therefore, in this work it is presented an online discrete-time training algorithm based on a super-twisting (ST) differentiator from sliding mode theory. Due to, sliding mode differentiators can estimate derivatives in finite time, the proposed training algorithm does not require to compute of the derivatives, unlike conventional training algorithms. The proposed training algorithm is implemented for the training of a recurrent high-order neural network (RHONN) identifier in a series-parallel configuration, and its performance is compared with the results using the extended Kalman filter (EKF) training algorithm. Simulation results of the RHONN identifier for the Lorenz system are presented

Consensus of networks of nonidentical robots with flexible joints, variable time--delays and unmeasurable velocities

The present paper proposes two controllers for solving a consensus problem to a given desired position of networks composed of a class of under actuated mechanical systems: flexible joints robots. One of the controllers makes use of joint (motor) velocity signals while the other only uses joint positions. The only assumption on the directed and weighted interconnection graph is that it is connected. Further, the interconnection may induce variable time–delays. The paper presents some experiments, using three 3-Degrees of Freedom manipulators, which show the performance of the proposed approaches.Postprint (published version